July 10, 2021
8:30 am / 10:00 am
Sequential learning addresses the problem of allocating resources under cost constraints, and sometimes lack of information. This class of problems is ubiquitous for instance in machine learning, operations research or econometrics. In particular, it is at the heart of Reinforcement Learning (RL), a learning paradigm where the agent learns via trial-and-error. In fact, a key difficulty for the learner is to decide how to explore the space of actions while trying to maximize a certain reward metric, thus facing an exploration-versus-exploitation trade-off.